Field
Embodiments of the present invention generally relate to a system and method for information management and particularly to a system and method for information sharing in an enterprise.
Description of Related Art
Contact centers are employed by many enterprises to service inbound and outbound contacts from customers. A typical contact center includes a switch and/or server to receive and route incoming packet-switched and/or circuit-switched contacts and one or more resources, such as human agents and automated resources (e.g., Interactive Voice Response units (IVR)), to service the incoming contacts. Contact centers distribute contacts, whether inbound or outbound, for servicing to any suitable resource according to predefined criteria. In many existing systems, the criteria for servicing the contact from the moment that the contact center becomes aware of the contact until the contact is connected to an agent are client or operator-specifiable (i.e., programmable by the operator of the contact center), via a capability called vectoring. In present-day Automatic Call Distributions (ACDs) when the ACD system detects that an agent has become available to handle a contact, the ACD system identifies all predefined contact-handling queues for the agent (usually in some order of priority) and delivers to the agent with the highest-priority, oldest contact that matches the agent's highest-priority queue. Generally, the only condition that results in a contact not being delivered to an available agent is that there are no contacts waiting to be handled.
The primary objective of contact center management is to ultimately maximize contact center performance and profitability. An ongoing challenge in contact center administration is monitoring and optimizing contact center efficiency. The contact center efficiency is generally measured in two ways that are service level and match rate.
Service level is one measurement of the contact center efficiency. Service level is typically determined by dividing the number of contacts accepted within the specified period by the number accepted plus the number that were not accepted, but completed in some other way (e.g., abandoned, given busy, canceled, flowed out). Of course, service level definitions may vary from one enterprise to another.
Match rate is another indicator used in measuring contact center efficiency. Match rate is usually determined by dividing the number of contacts accepted by a primary skill level agent within a period of time by the number of contacts accepted by any agent for a queue over the same period. An agent with a primary skill level is one that typically can handle contacts of a certain nature most effectively and/or efficiently. There are other contact center agents that may not be as proficient as the primary skill level agent, and those agents are identified either as secondary skill level agents or backup skill level agents. As can be appreciated, contacts received by a primary skill level agent are typically handled more quickly and accurately or effectively (e.g., higher revenue attained) than a contact received by a secondary or even backup skill level agent. Thus, it is an objective of most contact centers to optimize match rate along with the service level.
In addition to service level and match rate performance measures, contact centers use other Key Performance Indicators (“KPIs”), such as revenue, estimated, actual, or predicted wait time, average speed of answer, throughput, agent utilization, agent performance, agent responsiveness and the like, to calculate performance relative to their Service Level Agreements (“SLAs”). Operational efficiency is achieved when KPIs are managed near, but not above, SLA levels.
Throughput is a measure of the number of calls/contact requests or work requests that can be processed in a given amount of time. Agent utilization is a measure of how efficiently agents' time is being used. Customer service level is a measure of the time customers spend waiting for their work to be handled. Company contact center customers wish to provide service to as many requests as possible in a given amount of time, using the least number of agents to do so, and minimizing the wait time for their customers that can increase the service level agreement of the contact center.
Typically, the business goals of the contact center are achieved when the agents solve the customer's queries in a short interval of time with customer satisfaction. Sometimes, while solving the customer's queries, the agents need assistance or help from other agents or supervisors of the contact center. For this purpose, the agents may rely on their personal contacts that may help them to solve the customer's queries based on their existing relationships with the agents. For example, an agent “A” with a customer query related to mobile phones may ask for help from another agent “B” who is an expert in fixing mobile phones when the agent “A” is having a good relationship (e.g., friends) with the agent “B”. Otherwise, the agent may ask for assistance from the supervisor of the contact center. The supervisor may then provide a predefined script on the agent's computer screen to solve the customer's queries. Further, while using the predefined scripts the customer's queries may be escalated that further results in delays, higher costs, and customer dissatisfaction. Also, based on the supervisor' assistance (providing predefined scripts to agents), a credit and/or an incentive is rewarded to the supervisors. Conventionally, these credits can be assigned to the supervisors through mocks and dashboards. However, no rewards and/or incentives are provided to the agents such as, for utilizing predefined scripts provided by the supervisor.
Conventional techniques do not encourage the agents, supervisors or Subject Matter Experts (SMEs) to collaborate within the contact center to solve the complex customer's queries. Furthermore, these techniques do not build a social-networking based collaborative environment for sharing information with other agents of the contact center.
There is thus a need for a system and method for information sharing to encourage agents and experts to engage with the agents to solve complex customer-originating queries and provide incentives when doing so.